Szczegóły publikacji
Opis bibliograficzny
Estimation of absolute permeability using artificial neural networks (multilayer perceptrons) based on well logs and laboratory data from Silurian and Ordovician deposits in SE Poland / Sebastian WASZKIEWICZ, Paulina KRAKOWSKA-MADEJSKA, Edyta PUSKARCZYK // Acta Geophysica ; ISSN 1895-6572. — Tytuł poprz.: Acta Geophysica Polonica. — 2019 — vol. 67 iss. 6 spec. iss., s. 1885–1894. — Bibliogr. s. 1893–1894, Abstr. — Publikacja dostępna online od: 2019-09-04. — CAGG 2019 : conference "Challenges in Applied Geology and Geophysics" : Krakow, Poland, 10–13 September 2019
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 125943 |
|---|---|
| Data dodania do BaDAP | 2019-12-11 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s11600-019-00347-6 |
| Rok publikacji | 2019 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Acta Geophysica |
Abstract
Permeability is a property of rocks which refers to the ability of fluids to flow through each substance. It depends on several factors as pore shape and diameter. Also the presence and type of clay has a large influence on the permeability value. Permeability can be measured on rock sample in the laboratory by injecting fluid through the rock under known condition, but this provides only point information. Due to the dependence of the parameter on many factors, the deterministic estimation of permeability based on laboratory measurement and well logs is problematic. Many empirical methods for determining permeability are available in the literature and interpretation systems. An interesting approach to the problem is the use of artificial neural networks based on laboratory measurement and modern, high-resolution logging tools. The authors decided to use MLP artificial neural networks, which allow permeability estimation and can be used both in the test well and applied to neighbouring wells. The network was checked in several variants. Obtained results show the legitimacy of using artificial neural networks in the issue of estimating permeability. However, they also show limitations resulting from the lack of accurate data or influence of geological setting and processes.
